Scatter Plots

Scatter Plots are good to use:

When you have paired numerical data.
When your dependent variable may have multiple values for each value of your independent variable.
When trying to determine whether the two variables are related.

Basic Scatter Plot

import plotly.graph_objects as go
from plotly.offline import init_notebook_mode, plot
from IPython.core.display import display, HTML
init_notebook_mode(connected=True)
import numpy as np #Create random data with numpy

N = 1000
t = np.linspace(0, 10, 100)
y = np.cos(t)

#Make sure to specify markers in the mode
fig = go.Figure(data=go.Scatter(x=t, y=y, mode='markers'))

#show your plot
plot(fig, filename = 'figure8-3-1.html')
display(HTML('figure8-3-1.html'))

Customizing Scatter Plots

N = 1000
t = np.linspace(0, 10, 100)
y_0 = np.cos(t)
y_1 = np.sin(t)
y_2 = y_0 * 3
y_3 = y_1 / 3

fig = go.Figure()

# Add traces for each scatter
fig.add_trace(go.Scatter(x=t, y=y_0,
                    mode='markers',
                    name='Cosine', #name the marker
                    marker_color='lime')) #Change the marker color
fig.add_trace(go.Scatter(x=t, y=y_1,
                    mode='markers',
                    name='Sine',
                    marker_color = 'magenta'))
fig.add_trace(go.Scatter(x=t, y=y_2,
                    mode='markers',
                    marker_line_width=2, #change the marker width
                    marker_size=10, #Change the size of the marker
                    name='3 Cosine',
                    marker_color = 'hotpink'))

# Set options common to all traces (i.e title, background color)
fig.update_layout(title='Trigonometric FUNctions Click and Drag to Zoom In!',
                  plot_bgcolor = 'black')

plot(fig, filename = 'figure8-3-2.html')
display(HTML('figure8-3-2.html'))